The invention relates generally to the processing of color images, which may be used to assess tissue abnormality in a tissue image. In particular, the present techniques relate to the segmentation of color tissue images.
Digital microscopy has become increasingly important in pathology and morphology. Images of stained tissue slides may be obtained and used by pathologists to recognize abnormal tissue structures associated with cancer. For example, a prostate cancer diagnosis is typically established by histopathology using hematoxylin and eosin (H&E) stained tissue sections, which are evaluated by a pathologist to subjectively assess the cancer state or grade. A pathologist's assessment of the cancer stage may be based upon gland and nuclei distributions and morphological features observed in an image of the cancerous tissue and how these distributions and features differ from those of a normal tissue image. However, human pattern recognition may be time consuming and inefficient because of the number of new cancer cases each year and the limited resources, such as the number of pathologists available.
To improve throughput, tissue microarrays (TMA) may be used for pathology research. In this approach, tissue cores from different patients are embedded in a paraffin block and sliced to give multiple registered arrays. These multiple tissue cores are simultaneously processed to remove staining variability and to reduce labor. However, even after staining variability is removed, an accurate and efficient evaluation may still require segmentation of features of interest in the tissue slides. Image segmentation may generally involve splitting an image into several components and assigning each pixel in the image to a respective component. Specifically, segmentation may be useful for classifying tissue image elements, such as pixels or larger image structures, into useful groups or categories.
Manual segmentation of a tissue image may be extremely time intensive. Moreover, the manual analysis should be done by an expert pathologist, whose time is limited and valuable. Additionally, automated segmentation methods often result in inaccurately segmented images. In particular, current techniques for automated segmentation of images are often unsuitable for use on color images, such as stained images of tissue, due to the interdependence of the color components of each pixel. In addition, such automated segmentation techniques may be computationally intensive. Therefore, a more efficient image segmenting process for tissue images is desired.